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* Expose misc ops at package level

* Adding documentation to the ops exposed

Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* Add docs for PILToTensor

* Update docs/source/transforms.rst
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* add best weights and x_1_6, x_3_2, y_1_6, y_3_2, y_32 weights

* add best weights and x_1_6, x_3_2, y_1_6, y_3_2, y_32 weights

* add weights for x_16gf, x_32gf, y_16gf

* add ufmt as code formatter

* cleanup

* quote ufmt requirement

* split imports into more groups

* regenerate circleci config

* fix CI

* clarify local testing utils section

* use ufmt pre-commit hook

* split relative imports into local category

* Revert "split relative imports into local category"

This reverts commit f2e224cde2008c56c9347c1f69746d39065cdd51.

* pin black and usort dependencies

* fix local test utils detection

* fix ufmt rev

* add reference utils to local category

* fix usort config

* remove custom categories sorting

* Run pre-commit without fixing flake8

* got a double import in merge
Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* draft commit

* Polish and add corresponding test

* Update docs

* Update torchvision/models/feature_extraction.py

* Update docs/source/feature_extraction.rst
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* initial code

* add SqueezeExcitation

* initial code

* add SqueezeExcitation

* add SqueezeExcitation

* regnet blocks, stems and model definition

* nit

* add fc layer

* use Callable instead of Enum for block, stem and activation

* add regnet_x and regnet_y model build functions, add docs

* remove unused depth

* use BN/activation constructor and ConvBNActivation

* add expected test pkl files

* allow custom activation in SqueezeExcitation

* use ReLU as the default activation

* initial code

* add SqueezeExcitation

* initial code

* add SqueezeExcitation

* add SqueezeExcitation

* regnet blocks, stems and model definition

* nit

* add fc layer

* use Callable instead of Enum for block, stem and activation

* add regnet_x and regnet_y model build functions, add docs

* remove unused depth

* use BN/activation constructor and ConvBNActivation

* reuse SqueezeExcitation from efficientnet

* refactor RegNetParams into BlockParams

* use nn.init, replace np with torch

* update README

* construct model with stem, block, classifier instances

* Revert "construct model with stem, block, classifier instances"

This reverts commit 850f5f3ed01a2a9b36fcbf8405afd6e41d2e58ef.

* remove unused blocks

* support scaled model

* fuse into ConvBNActivation

* make reset_parameters private

* fix type errors

* fix for unit test

* add pretrained weights for 6 variant models, update docs

* start writing example

* Update example

* Add PenFudan Files

* Update example

* Remove unused files

* Update file and adopt changes

* Create links, fix float
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* ops.masks_to_bounding_boxes

* test fixtures

* unit test

* ignore lint e201 and e202 for in-lined matrix

* ignore e121 and e241 linting rules for in-lined matrix

* draft gallery example text

* removed type annotations from pytest fixtures

* inlined fixture

* renamed masks_to_bounding_boxes to masks_to_boxes

* reformat inline array

* import cleanup

* moved masks_to_boxes into boxes module

* docstring cleanup

* updated docstring

* fix formatting issue

* gallery example

* use torch

* use torch

* use torch

* use torch

* updated docs and test

* cleanup

* updated import

* use torch

* Update gallery/plot_repurposing_annotations.py
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* Update gallery/plot_repurposing_annotations.py
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* Update gallery/plot_repurposing_annotations.py
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* Autodoc

* use torch instead of numpy in tests

* fix build_docs failure

* Closing quotes.
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* Added LFW Dataset

* Added dataset to list in __init__.py

* Updated lfw.py
* Created a common superclass for people and pairs type datatsets
* corrected the .download() method

* Added docstrings and updated datasets.rst

* Wrote tests for LFWPeople and LFWPairs

* Resolved mypy error: Need type annotation for "data"

* Updated inject_fake_data method for LFWPeople

* Updated tests for LFW

* Updated LFW tests and minor changes in lfw.py

* Updated LFW
* Added functionality for 10-fold validation view
* Optimized the code so to replace repeated lines by method in super
  class
* Updated LFWPeople to get classes from all lfw-names.txt rather than
  just the classes fron trainset

* Updated lfw.py and tests
* Updated inject_fake_data method to create 10fold fake data
* Minor changes in docstring and extra_repr

* resolved py lint errors

* Added checksums for annotation files

* Minor changes in test

* Updated docstrings, defaults and minor changes in test

* Removed 'os.path.exists' check
Co-authored-by: ABD-01 <myac931@gmai.com>
Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* add pre-commit hooks

* ignore yamls in packaging/*

* add pre-commit to contributing guide lines

* Update CONTRIBUTING.md
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* remove some hooks

* fix docstrings

* fix end of files
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* add fx feature extraction util

* Make it possible to use train and eval mode

* FX feature extraction - Tweaks and small bug fixes

* FX feature extraction - add tests

* move to feature_extraction.py, add LeafModuleAwareTracer, add docs

* Tweaks to docs

* addressing latest round of feedback

* undo line spacing changes

* change type hints in docstrings

* fix sphinx indentation

* expose feature_extraction

* add maskrcnn example

* add api refernce subheading

* address latest review notes, refactor names, fix regex, cosmetics

* Add back efficientnet to models

* fix tests for effnet

* fix linting issue

* fix test tracer kwargs
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Initial Proposal

* Tensor Save Test + Test Name Fix

* Formatting + removing unused argument

* fix old argument

* fix isnan check error + indexing error with jit

* Fix Flake8 error.

* Fix MyPy error.

* Fix Flake8 error.

* Fix PyTorch JIT error in UnitTests due to type annotation.

* Fixing tests.

* Removing type ignore.

* Adding support of ta_wide in references.

* Move methods in classes.

* Moving new classes to the bottom.

* Specialize to TA to TAwide

* Add missing type

* Fixing lint

* Fix doc

* Fix search space of TrivialAugment.
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Vasilis Vryniotis <vvryniotis@fb.com>

Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Adding docs for RandAugment.

* Fix docs.

* Adding code skeleton

* Adding MBConvConfig.

* Extend SqueezeExcitation to support custom min_value and activation.

* Implement MBConv.

* Replace stochastic_depth with operator.

* Adding the rest of the EfficientNet implementation

* Update torchvision/models/efficientnet.py

* Replacing 1st activation of SE with SiLU.

* Adding efficientnet_b3.

* Replace mobilenetv3 assets with custom.

* Switch to standard sigmoid and reconfiguring BN.

* Reconfiguration of efficientnet.

* Add repr

* Add weights.

* Update weights.

* Adding B5-B7 weights.

* Update docs and hubconf.

* Fix doc link.

* Fix typo on comment.

* Adding operator.

* Adding tests

* switching order of `p` and `mode`.

* Remove seed setting.

* Replace stats import with pytest.importorskip.

* Fix doc

* Apply suggestions from code review
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Fixing indentation.

* Adding operator in the documentation.

* Fixing lint
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add iNaturalist dataset

* Add download support

* address comments
Co-authored-by: dgenzel <dgenzel@fb.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* fix

* add docs for VisionDataset

* add getitem

* add deprecated note to transform

* Update torchvision/datasets/vision.py
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* fix docs

* Update torchvision/datasets/vision.py

* flake8
Co-authored-by: Nicolas Hug <nicolashug@fb.com>
Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Fix io imports and expose methods

* fix fmt
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Partial implementation of SSDlite.

* Add normal init and BN hyperparams.

* Refactor to keep JIT happy

* Completed SSDlite.

* Fix lint

* Update todos

* Add expected file in repo.

* Use C4 expansion instead of C4 output.

* Change scales formula for Default Boxes.

* Add cosine annealing on trainer.

* Make T_max count epochs.

* Fix test and handle corner-case.

* Add support of support width_mult

* Add ssdlite presets.

* Change ReLU6, [-1,1] rescaling, backbone init & no pretraining.

* Use _reduced_tail=True.

* Add sync BN support.

* Adding the best config along with its weights and documentation.

* Make mean/std configurable.

* Fix not implemented for half exception

* Add auto-augment introduced transforms in docs.

* Adding examples in Gallery.

* Make posterize more extreme.

* Early skeleton of API.

* Adding MultiFeatureMap and vgg16 backbone.

* Making vgg16 backbone same as paper.

* Making code generic to support all vggs.

* Moving vgg's extra layers a separate class + L2 scaling.

* Adding header vgg layers.

* Fix maxpool patching.

* Refactoring code to allow for support of different backbones & sizes:
- Skeleton for Default Boxes generator class
- Dynamic estimation of configuration when possible
- Addition of types

* Complete the implementation of DefaultBox generator.

* Replace randn with empty.

* Minor refactoring

* Making clamping between 0 and 1 optional.

* Change xywh to xyxy encoding.

* Adding parameters and reusing objects in constructor.

* Temporarily inherit from Retina to avoid dup code.

* Implement forward methods + temp workarounds to inherit from retina.

* Inherit more methods from retinanet.

* Fix type error.

* Add Regression loss.

* Fixing JIT issues.

* Change JIT workaround to minimize new code.

* Fixing initialization bug.

* Add classification loss.

* Update todos.

* Add weight loading support.

* Support SSD512.

* Change kernel_size to get output size 1x1

* Add xavier init and refactoring.

* Adding unit-tests and fixing JIT issues.

* Add a test for dbox generator.

* Remove unnecessary import.

* Workaround on GeneralizedRCNNTransform to support fixed size input.

* Remove unnecessary random calls from the test.

* Remove more rand calls from the test.

* change mapping and handling of empty labels

* Fix JIT warnings.

* Speed up loss.

* Convert 0-1 dboxes to original size.

* Fix warning.

* Fix tests.

* Update comments.

* Fixing minor bugs.

* Introduce a custom DBoxMatcher.

* Minor refactoring

* Move extra layer definition inside feature extractor.

* handle no bias on init.

* Remove fixed image size limitation

* Change initialization values for bias of classification head.

* Refactoring and update test file.

* Adding ResNet backbone.

* Minor refactoring.

* Remove inheritance of retina and general refactoring.

* SSD should fix the input size.

* Fixing messages and comments.

* Silently ignoring exception if test-only.

* Update comments.

* Update regression loss.

* Restore Xavier init everywhere, update the negative sampling method, change the clipping approach.

* Fixing tests.

* Refactor to move the losses from the Head to the SSD.

* Removing resnet50 ssd version.

* Adding support for best performing backbone and its config.

* Refactor and clean up the API.

* Fix lint

* Update todos and comments.

* Adding RandomHorizontalFlip and RandomIoUCrop transforms.

* Adding necessary checks to our tranforms.

* Adding RandomZoomOut.

* Adding RandomPhotometricDistort.

* Moving Detection transforms to references.

* Update presets

* fix lint

* leave compose and object

* Adding scaling for completeness.

* Adding params in the repr

* Remove unnecessary import.

* minor refactoring

* Remove unnecessary call.

* Give better names to DBox* classes

* Port num_anchors estimation in generator

* Remove rescaling and fix presets

* Add the ability to pass a custom head and refactoring.

* fix lint

* Fix unit-test

* Update todos.

* Change mean values.

* Change the default parameter of SSD to train the full VGG16 and remove the catch of exception for eval only.

* Adding documentation

* Adding weights and updating readmes.

* Update the model weights with a more performing model.

* Adding doc for head.

* Restore import.